{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2026-04-08T02:18:46.436753900Z",
     "start_time": "2026-04-08T02:18:46.408122700Z"
    }
   },
   "source": [
    "import os\n",
    "os.environ['FOR_DISABLE_CONSOLE_CTRL_HANDLER'] = '1'\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Subset, TensorDataset\n",
    "from torchvision import datasets, transforms\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "import time\n",
    "from math import ceil"
   ],
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T02:02:41.594903300Z",
     "start_time": "2026-04-08T02:02:41.562731100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "SEED = 42\n",
    "batch_size = 32\n",
    "lr = 0.001\n",
    "epochs = 50\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "torch.manual_seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomAffine(degrees=5, translate=(0.05, 0.05)),\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "transform_val = transforms.Compose([transforms.ToTensor()])\n",
    "plt.rcParams.update({'font.size': 13})"
   ],
   "id": "3076ddbcddc03e61",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:13:20.068211500Z",
     "start_time": "2026-04-08T01:13:19.911495400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "full_train_raw = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform_val)\n",
    "test_raw = datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform_val)\n",
    "\n",
    "labels = full_train_raw.targets.numpy()\n",
    "\n",
    "sss = StratifiedShuffleSplit(n_splits=1, train_size=12000, test_size=2000, random_state=42)\n",
    "train_idx, val_idx = next(sss.split(np.zeros(len(labels)), labels))\n",
    "\n",
    "test_labels_raw = test_raw.targets.numpy()\n",
    "sss_test = StratifiedShuffleSplit(n_splits=1, test_size=8000, random_state=42)\n",
    "_, test_idx_final = next(sss_test.split(np.zeros(len(test_labels_raw)), test_labels_raw))\n",
    "\n",
    "full_train_aug = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform_train)\n",
    "full_train_plain = datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform_val)\n",
    "\n",
    "train_dataset = Subset(full_train_aug, train_idx)   # 12000\n",
    "val_dataset   = Subset(full_train_plain, val_idx)   # 2000\n",
    "test_dataset  = Subset(test_raw, test_idx_final)    # 8000\n",
    "\n",
    "print(f\"训练集大小: {len(train_dataset)}\")\n",
    "print(f\"验证集大小: {len(val_dataset)}\")\n",
    "print(f\"测试集大小: {len(test_dataset)}\")\n"
   ],
   "id": "cee6033acc8b0be1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: 12000\n",
      "验证集大小: 2000\n",
      "测试集大小: 8000\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:13:20.111005700Z",
     "start_time": "2026-04-08T01:13:20.069216200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ],
   "id": "2f8187f74f3cea50",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:13:20.153872100Z",
     "start_time": "2026-04-08T01:13:20.112557Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## mlp模型定义\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, input_size=784, hidden_sizes=(256,128), output_size=10, dropout=0.2):\n",
    "        super(MLP, self).__init__()\n",
    "        layers = []\n",
    "        prev = input_size\n",
    "        for hs in hidden_sizes:\n",
    "            layers.append(nn.Linear(prev, hs))\n",
    "            layers.append(nn.ReLU())\n",
    "            layers.append(nn.Dropout(dropout))\n",
    "            prev = hs\n",
    "        self.features = nn.Sequential(*layers)\n",
    "        self.classifier = nn.Linear(prev, output_size)\n",
    "        self._initialize_weights()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(x.size(0), -1)\n",
    "        feat = self.features(x)\n",
    "        out = self.classifier(feat)\n",
    "        return out,feat\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Linear):\n",
    "                # 使用He初始化\n",
    "                nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)\n"
   ],
   "id": "cee84103c979056b",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:13:20.264092200Z",
     "start_time": "2026-04-08T01:13:20.190332100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## CNN模型定义\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, num_classes=10):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv_layers = nn.Sequential(\n",
    "            nn.Conv2d(1, 32, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            nn.Conv2d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2, 2)\n",
    "        )\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64 * 7 * 7, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "        )\n",
    "        self.classifier = nn.Linear(256, num_classes)\n",
    "        self._initialize_weights()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv_layers(x)\n",
    "        feat = self.fc(x)\n",
    "        out = self.classifier(feat)\n",
    "        return out,feat\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                # 卷积层使用He初始化\n",
    "                nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                # 线性层使用He初始化\n",
    "                nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)"
   ],
   "id": "66ba6c16cae8de22",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:13:20.315751100Z",
     "start_time": "2026-04-08T01:13:20.265687900Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## 带多头注意力的CNN模型定义\n",
    "class MultiHeadSelfAttention(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, dropout=0.1):\n",
    "        super().__init__()\n",
    "        assert embed_dim % num_heads == 0, \"embed_dim must be divisible by num_heads\"\n",
    "        self.num_heads = num_heads\n",
    "        self.head_dim = embed_dim // num_heads\n",
    "        self.scale = self.head_dim ** -0.5\n",
    "\n",
    "        self.qkv = nn.Linear(embed_dim, embed_dim * 3)\n",
    "        self.attn_drop = nn.Dropout(dropout)\n",
    "        self.proj = nn.Linear(embed_dim, embed_dim)\n",
    "        self.proj_drop = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x, return_attn=False):\n",
    "        B, N, D = x.shape  # (batch, num_patches, embed_dim)\n",
    "        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)\n",
    "        q, k, v = qkv[0], qkv[1], qkv[2]  # (B, num_heads, N, head_dim)\n",
    "\n",
    "        attn = (q @ k.transpose(-2, -1)) * self.scale\n",
    "        attn = attn.softmax(dim=-1)\n",
    "        attn = self.attn_drop(attn)\n",
    "\n",
    "        out = (attn @ v).transpose(1, 2).reshape(B, N, D)\n",
    "        out = self.proj(out)\n",
    "        out = self.proj_drop(out)\n",
    "        if return_attn:\n",
    "            return out, attn\n",
    "        else:\n",
    "            return out\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, mlp_ratio=4, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.norm1 = nn.LayerNorm(embed_dim)\n",
    "        self.norm1 = nn.LayerNorm(embed_dim)\n",
    "        self.attn = MultiHeadSelfAttention(embed_dim, num_heads, dropout)\n",
    "        self.norm2 = nn.LayerNorm(embed_dim)\n",
    "        mlp_hidden_dim = int(embed_dim * mlp_ratio)\n",
    "        self.mlp = nn.Sequential(\n",
    "            nn.Linear(embed_dim, mlp_hidden_dim),\n",
    "            nn.GELU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(mlp_hidden_dim, embed_dim),\n",
    "            nn.Dropout(dropout)\n",
    "        )\n",
    "\n",
    "    def forward(self, x, return_attn=False):\n",
    "        if return_attn:\n",
    "            attn_out, attn = self.attn(self.norm1(x), return_attn=True)\n",
    "            x = x + attn_out\n",
    "            x = x + self.mlp(self.norm2(x))\n",
    "            return x, attn\n",
    "        else:\n",
    "            x = x + self.attn(self.norm1(x))\n",
    "            x = x + self.mlp(self.norm2(x))\n",
    "            return x\n",
    "\n",
    "\n",
    "class HybridCNN(nn.Module):\n",
    "    def __init__(self, img_size=28, in_channels=1, patch_size=7, embed_dim=16,num_heads=8, num_layers=6, num_classes=10, cnn_out_channels=64):\n",
    "        super().__init__()\n",
    "        self.img_size = (img_size, img_size) if isinstance(img_size, int) else img_size\n",
    "        self.patch_size = (patch_size, patch_size) if isinstance(patch_size, int) else patch_size\n",
    "\n",
    "        # 确保图像尺寸能被 patch_size 整除\n",
    "        assert self.img_size[0] % self.patch_size[0] == 0 and self.img_size[1] % self.patch_size[1] == 0\n",
    "        self.num_patches_h = self.img_size[0] // self.patch_size[0]\n",
    "        self.num_patches_w = self.img_size[1] // self.patch_size[1]\n",
    "        self.num_patches = self.num_patches_h * self.num_patches_w\n",
    "\n",
    "        self.cnn = nn.Sequential(\n",
    "            nn.Conv2d(in_channels, 32, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(32, cnn_out_channels, kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm2d(cnn_out_channels),\n",
    "            nn.ReLU(inplace=True),\n",
    "        )\n",
    "\n",
    "        patch_dim = cnn_out_channels * self.patch_size[0] * self.patch_size[1]\n",
    "        self.patch_embed = nn.Linear(patch_dim, embed_dim)\n",
    "\n",
    "        self.transformer_blocks = nn.ModuleList([\n",
    "            TransformerBlock(embed_dim, num_heads) for _ in range(num_layers)\n",
    "        ])\n",
    "        self.norm = nn.LayerNorm(embed_dim)\n",
    "\n",
    "        self.head = nn.Linear(embed_dim, num_classes)\n",
    "        self._init_weights()\n",
    "\n",
    "    def forward(self, x, return_attn=False):\n",
    "        B, C, H, W = x.shape\n",
    "        assert H == self.img_size[0] and W == self.img_size[1], f\"Input size {H}x{W} != {self.img_size}\"\n",
    "\n",
    "        feat = self.cnn(x)  # (B, cnn_out_channels, H, W)\n",
    "        ph, pw = self.patch_size\n",
    "        patches = feat.unfold(2, ph, ph).unfold(3, pw, pw)\n",
    "        patches = patches.contiguous().view(B, -1, ph, pw)\n",
    "        patches = feat.unfold(2, ph, ph).unfold(3, pw, pw)\n",
    "        patches = patches.permute(0, 2, 3, 1, 4, 5).contiguous()\n",
    "        patches = patches.view(B, self.num_patches, -1)\n",
    "        patch_embeds = self.patch_embed(patches)\n",
    "\n",
    "        x_seq = patch_embeds\n",
    "        attentions = []\n",
    "        for blk in self.transformer_blocks:\n",
    "            if return_attn:\n",
    "                x_seq, attn = blk(x_seq, return_attn=True)\n",
    "                attentions.append(attn)\n",
    "            else:\n",
    "                x_seq = blk(x_seq)\n",
    "        x_seq = self.norm(x_seq)\n",
    "        global_feat = x_seq.mean(dim=1)\n",
    "\n",
    "        logits = self.head(global_feat)\n",
    "\n",
    "        if return_attn:\n",
    "            return logits, global_feat, attentions\n",
    "        else:\n",
    "            return logits, global_feat\n",
    "\n",
    "    def _init_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                nn.init.constant_(m.weight, 1)\n",
    "                nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                nn.init.xavier_uniform_(m.weight)\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.LayerNorm):\n",
    "                nn.init.constant_(m.weight, 1)\n",
    "                nn.init.constant_(m.bias, 0)"
   ],
   "id": "6cd2dd8d980e4cd6",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:13:20.339588600Z",
     "start_time": "2026-04-08T01:13:20.315751100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## 训练评估部分\n",
    "def get_model_class_name(model):\n",
    "    return type(model).__name__\n",
    "\n",
    "def train_model(model, train_loader, val_loader, epochs=20, lr=0.001, device='cuda',model_type='cnn'):\n",
    "    model = model.to(device)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=5e-5)\n",
    "    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)\n",
    "\n",
    "    train_losses, val_losses = [], []\n",
    "    train_accs, val_accs = [], []\n",
    "    best_val_acc = 0.0\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        for inputs, labels in train_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            if model_type == 'mlp':\n",
    "                outputs, _ = model(inputs)\n",
    "            elif model_type == 'hybrid':\n",
    "                outputs, _ = model(inputs, return_attn=False)\n",
    "            elif model_type == 'cnn':\n",
    "                outputs, _ = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item() * inputs.size(0)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "        epoch_loss = running_loss / len(train_loader.dataset)\n",
    "        epoch_acc = correct / total\n",
    "        train_losses.append(epoch_loss)\n",
    "        train_accs.append(epoch_acc)\n",
    "\n",
    "        # 验证\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        val_correct = 0\n",
    "        val_total = 0\n",
    "        with torch.no_grad():\n",
    "            for inputs, labels in val_loader:\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\n",
    "                if model_type == 'mlp':\n",
    "                    outputs, _ = model(inputs)\n",
    "                elif model_type == 'hybrid':\n",
    "                    outputs, _ = model(inputs, return_attn=False)\n",
    "                elif model_type == 'cnn':\n",
    "                    outputs, _ = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                val_loss += loss.item() * inputs.size(0)\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "                val_total += labels.size(0)\n",
    "                val_correct += (predicted == labels).sum().item()\n",
    "        val_loss = val_loss / len(val_loader.dataset)\n",
    "        val_acc = val_correct / val_total\n",
    "        val_losses.append(val_loss)\n",
    "        val_accs.append(val_acc)\n",
    "\n",
    "        scheduler.step(val_loss)\n",
    "\n",
    "        if val_acc > best_val_acc:\n",
    "            best_val_acc = val_acc\n",
    "            torch.save(model.state_dict(), f'best_model_{get_model_class_name(model)}.pth')\n",
    "\n",
    "        print(\n",
    "            f'Epoch {epoch + 1:2d}/{epochs} | Train Loss: {epoch_loss:.4f} | Train Acc: {epoch_acc:.4f} | Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}')\n",
    "\n",
    "    return train_losses, val_losses, train_accs, val_accs\n",
    "\n",
    "##模型评估函数\n",
    "def evaluate(model, test_loader, device='cuda', model_type='cnn'):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in test_loader:\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            if model_type == 'mlp':\n",
    "                outputs, _ = model(inputs)\n",
    "            elif model_type == 'hybrid':\n",
    "                outputs, _ = model(inputs, return_attn=False)\n",
    "            elif model_type == 'cnn':\n",
    "                outputs, _ = model(inputs)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    accuracy = correct / total\n",
    "    return accuracy"
   ],
   "id": "5e21cb8b0b18176d",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T02:20:00.772372800Z",
     "start_time": "2026-04-08T02:20:00.723724200Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## 可视化部分\n",
    "def get_model_feature(model, loader, device, model_type):\n",
    "    model.eval()\n",
    "    feats_list = []\n",
    "    labels_list = []\n",
    "    images_list = []\n",
    "    with torch.no_grad():\n",
    "        for inputs, labels in loader:\n",
    "            inputs = inputs.to(device)\n",
    "            if model_type == 'mlp':\n",
    "                _, feat = model(inputs)\n",
    "            elif model_type == 'hybrid':\n",
    "                _, feat = model(inputs, return_attn=False)\n",
    "            else:  # cnn\n",
    "                _, feat = model(inputs)\n",
    "            feats_list.append(feat.cpu())\n",
    "            labels_list.append(labels)\n",
    "            images_list.append(inputs.cpu())\n",
    "    feats = torch.cat(feats_list, dim=0)\n",
    "    labels = torch.cat(labels_list, dim=0)\n",
    "    images = torch.cat(images_list, dim=0)\n",
    "    return feats, labels, images\n",
    "\n",
    "\n",
    "##最近邻分析可视化\n",
    "def visualize_nearest_neighbors(model, train_loader, test_loader, device, model_type, k=5, save_path=None):\n",
    "    train_feats, train_labels, train_images = get_model_feature(model, train_loader, device, model_type)\n",
    "    test_feats, test_labels, test_images = get_model_feature(model, test_loader, device, model_type)\n",
    "\n",
    "    num_classes = 10\n",
    "    test_indices = []\n",
    "    for c in range(num_classes):\n",
    "        idx = (test_labels == c).nonzero(as_tuple=True)[0]\n",
    "        if len(idx) > 0:\n",
    "            test_indices.append(idx[0].item())\n",
    "    assert len(test_indices) == num_classes\n",
    "\n",
    "    fig, axes = plt.subplots(num_classes, k+1, figsize=((k+1)*3, num_classes*3))\n",
    "    if num_classes == 1:\n",
    "        axes = axes.reshape(1, -1)\n",
    "\n",
    "    for i, tidx in enumerate(test_indices):\n",
    "        test_feat = test_feats[tidx:tidx+1]\n",
    "        test_img = test_images[tidx]\n",
    "        test_lbl = test_labels[tidx].item()\n",
    "        dists = torch.cdist(test_feat, train_feats, p=2).squeeze(0)\n",
    "        nearest = dists.topk(k, largest=False).indices\n",
    "\n",
    "        ax = axes[i, 0]\n",
    "        ax.imshow(test_img.squeeze(), cmap='gray')\n",
    "        ax.set_title(f'Test label:{test_lbl}')\n",
    "        ax.axis('off')\n",
    "\n",
    "        for j, nid in enumerate(nearest):\n",
    "            ax = axes[i, j+1]\n",
    "            ax.imshow(train_images[nid].squeeze(), cmap='gray')\n",
    "            ax.set_title(f'Train label:{train_labels[nid].item()}')\n",
    "            ax.axis('off')\n",
    "\n",
    "    #plt.suptitle(f'{model_type.upper()} - Nearest Neighbors per Class', y=1.02)\n",
    "    plt.tight_layout()\n",
    "    if save_path:\n",
    "        plt.savefig(save_path, bbox_inches='tight')\n",
    "    else:\n",
    "        plt.savefig(f'{model_type}_nearest_neighbors_combined.png', bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "## 第一个卷积核权重可视化\n",
    "def visualize_first_conv_filters(model, model_type):\n",
    "    first_conv = None\n",
    "    for m in model.modules():\n",
    "        if isinstance(m, nn.Conv2d):\n",
    "            first_conv = m\n",
    "            break\n",
    "    if first_conv is None:\n",
    "        print(\"未找到卷积层\")\n",
    "        return\n",
    "    weights = first_conv.weight.data.cpu().numpy()\n",
    "    n_filters = min(32, weights.shape[0])\n",
    "    fig, axes = plt.subplots(4, 8, figsize=(12,6))\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        if i < n_filters:\n",
    "            filt = weights[i,0,:,:]\n",
    "            ax.imshow(filt, cmap='gray')\n",
    "            ax.axis('off')\n",
    "    #plt.suptitle('First Convolutional Layer Filters')\n",
    "    plt.savefig(f'{model_type}_first_conv_filters.png')\n",
    "    plt.show()\n",
    "\n",
    "## 第一个卷积核特征图可视化\n",
    "def visualize_first_conv_feature_maps(model, image, device, model_type):\n",
    "    activation = {}\n",
    "    def hook_fn(module, inp, out):\n",
    "        activation['map'] = out.detach()\n",
    "    first_conv = None\n",
    "    for m in model.modules():\n",
    "        if isinstance(m, nn.Conv2d):\n",
    "            first_conv = m\n",
    "            break\n",
    "    if first_conv is None:\n",
    "        print(\"未找到卷积层\")\n",
    "        return\n",
    "    handle = first_conv.register_forward_hook(hook_fn)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        if model_type == 'hybrid':\n",
    "            _, _ = model(image.unsqueeze(0).to(device), return_attn=False)\n",
    "        else:\n",
    "            _, _ = model(image.unsqueeze(0).to(device))\n",
    "    handle.remove()\n",
    "    feat_map = activation['map'].squeeze().cpu().numpy()\n",
    "    n_maps = min(16, feat_map.shape[0])\n",
    "    fig, axes = plt.subplots(4,4, figsize=(10,10))\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        if i < n_maps:\n",
    "            ax.imshow(feat_map[i], cmap='viridis')\n",
    "            ax.axis('off')\n",
    "    #plt.suptitle('Feature maps after first conv layer')\n",
    "    plt.savefig(f'{model_type}_first_conv_feature_maps.png')\n",
    "    plt.show()\n",
    "\n",
    "##注意力头可视化\n",
    "def visualize_transformer_attention_heads(model, image, device, model_type, layer_idx=0):\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        _, _, attentions = model(image.unsqueeze(0).to(device), return_attn=True)\n",
    "\n",
    "    attn_tensor = attentions[layer_idx][0]          # [num_heads, N, N]\n",
    "    num_heads = attn_tensor.shape[0]\n",
    "    N = attn_tensor.shape[1]\n",
    "    h_patches = model.num_patches_h\n",
    "    w_patches = model.num_patches_w\n",
    "    ph, pw = model.patch_size\n",
    "    img_np = image.squeeze().cpu().numpy()\n",
    "\n",
    "    # 计算右侧子图的布局：2 行，每行 ceil(num_heads/2) 列\n",
    "    cols = ceil(num_heads / 2)\n",
    "    rows = 2\n",
    "\n",
    "    # 使用 GridSpec 实现左侧原图跨两行，右侧为 2×cols 网格\n",
    "    fig = plt.figure(figsize=(3 + 3 * cols, 3 * rows), constrained_layout=True)\n",
    "    gs = fig.add_gridspec(rows, cols + 1)   # +1 为左侧原图列\n",
    "\n",
    "    # 左侧原图：占据全部两行\n",
    "    ax_orig = fig.add_subplot(gs[:, 0])\n",
    "    ax_orig.imshow(img_np, cmap='gray')\n",
    "    ax_orig.set_title('Original')\n",
    "    ax_orig.axis('off')\n",
    "\n",
    "    # 逐个绘制注意力头\n",
    "    head_idx = 0\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            if head_idx >= num_heads:\n",
    "                # 多余的子图隐藏\n",
    "                ax = fig.add_subplot(gs[r, c+1])\n",
    "                ax.axis('off')\n",
    "                continue\n",
    "\n",
    "            attn_map = attn_tensor[head_idx]          # [N, N]\n",
    "            patch_attn = attn_map.mean(dim=0)         # [N]\n",
    "            attn_img = np.zeros((model.img_size[0], model.img_size[1]))\n",
    "            for idx in range(N):\n",
    "                row = idx // w_patches\n",
    "                col = idx % w_patches\n",
    "                y_start = row * ph\n",
    "                x_start = col * pw\n",
    "                attn_img[y_start:y_start+ph, x_start:x_start+pw] = patch_attn[idx].item()\n",
    "\n",
    "            ax = fig.add_subplot(gs[r, c+1])\n",
    "            im = ax.imshow(attn_img, cmap='jet', vmin=0, vmax=1)\n",
    "            ax.set_title(f'Head {head_idx+1}')\n",
    "            ax.axis('off')\n",
    "            head_idx += 1\n",
    "\n",
    "    # 添加 colorbar\n",
    "    fig.colorbar(im, ax=fig.get_axes(), location='right', shrink=0.6)\n",
    "    plt.savefig(f'{model_type}_transformer_attention_heads.png')\n",
    "    plt.show()"
   ],
   "id": "52934337c304653c",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:14:52.594244100Z",
     "start_time": "2026-04-08T01:13:20.392324500Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## 训练MLP\n",
    "mlp = MLP(input_size=784, hidden_sizes=[256, 128], dropout=0.2)\n",
    "start_time = time.time()\n",
    "mlp_train_losses, mlp_val_losses, mlp_train_accs, mlp_val_accs = train_model(mlp, train_loader, val_loader, epochs=epochs,lr=lr,device=device,model_type='mlp')\n",
    "elapsed = time.time() - start_time\n",
    "mlp.load_state_dict(torch.load(f'best_model_{get_model_class_name(mlp)}.pth'))\n",
    "mlp_test_acc = evaluate(mlp, test_loader, device=device, model_type='mlp')\n",
    "total_params = sum(p.numel() for p in mlp.parameters())\n",
    "print(f\"MLP测试准确率: {mlp_test_acc:.4f}\")\n",
    "print(f\"训练耗时: {elapsed:.2f} 秒\")\n",
    "print(f\"Total parameters: {total_params:}\")"
   ],
   "id": "db46d803f0cbdf25",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_19824\\2557972199.py:67: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  scheduler.step(val_loss)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1/50 | Train Loss: 0.9464 | Train Acc: 0.6495 | Val Loss: 0.6147 | Val Acc: 0.7805\n",
      "Epoch  2/50 | Train Loss: 0.6995 | Train Acc: 0.7326 | Val Loss: 0.5647 | Val Acc: 0.7790\n",
      "Epoch  3/50 | Train Loss: 0.6319 | Train Acc: 0.7598 | Val Loss: 0.5137 | Val Acc: 0.8195\n",
      "Epoch  4/50 | Train Loss: 0.5910 | Train Acc: 0.7800 | Val Loss: 0.4616 | Val Acc: 0.8275\n",
      "Epoch  5/50 | Train Loss: 0.5783 | Train Acc: 0.7825 | Val Loss: 0.4894 | Val Acc: 0.8225\n",
      "Epoch  6/50 | Train Loss: 0.5460 | Train Acc: 0.7947 | Val Loss: 0.4674 | Val Acc: 0.8290\n",
      "Epoch  7/50 | Train Loss: 0.5411 | Train Acc: 0.7947 | Val Loss: 0.4482 | Val Acc: 0.8340\n",
      "Epoch  8/50 | Train Loss: 0.5226 | Train Acc: 0.8005 | Val Loss: 0.4641 | Val Acc: 0.8185\n",
      "Epoch  9/50 | Train Loss: 0.5135 | Train Acc: 0.8033 | Val Loss: 0.4246 | Val Acc: 0.8505\n",
      "Epoch 10/50 | Train Loss: 0.5042 | Train Acc: 0.8101 | Val Loss: 0.4502 | Val Acc: 0.8325\n",
      "Epoch 11/50 | Train Loss: 0.5047 | Train Acc: 0.8113 | Val Loss: 0.4334 | Val Acc: 0.8465\n",
      "Epoch 12/50 | Train Loss: 0.4898 | Train Acc: 0.8188 | Val Loss: 0.4268 | Val Acc: 0.8435\n",
      "Epoch 13/50 | Train Loss: 0.4927 | Train Acc: 0.8193 | Val Loss: 0.4245 | Val Acc: 0.8530\n",
      "Epoch 14/50 | Train Loss: 0.4816 | Train Acc: 0.8165 | Val Loss: 0.4289 | Val Acc: 0.8465\n",
      "Epoch 15/50 | Train Loss: 0.4712 | Train Acc: 0.8204 | Val Loss: 0.4193 | Val Acc: 0.8525\n",
      "Epoch 16/50 | Train Loss: 0.4740 | Train Acc: 0.8233 | Val Loss: 0.3933 | Val Acc: 0.8480\n",
      "Epoch 17/50 | Train Loss: 0.4675 | Train Acc: 0.8238 | Val Loss: 0.3989 | Val Acc: 0.8505\n",
      "Epoch 18/50 | Train Loss: 0.4623 | Train Acc: 0.8285 | Val Loss: 0.3772 | Val Acc: 0.8630\n",
      "Epoch 19/50 | Train Loss: 0.4647 | Train Acc: 0.8245 | Val Loss: 0.4141 | Val Acc: 0.8400\n",
      "Epoch 20/50 | Train Loss: 0.4439 | Train Acc: 0.8307 | Val Loss: 0.3939 | Val Acc: 0.8525\n",
      "Epoch 21/50 | Train Loss: 0.4453 | Train Acc: 0.8317 | Val Loss: 0.3831 | Val Acc: 0.8600\n",
      "Epoch 22/50 | Train Loss: 0.4431 | Train Acc: 0.8359 | Val Loss: 0.3800 | Val Acc: 0.8630\n",
      "Epoch 23/50 | Train Loss: 0.4484 | Train Acc: 0.8333 | Val Loss: 0.3962 | Val Acc: 0.8670\n",
      "Epoch 24/50 | Train Loss: 0.4429 | Train Acc: 0.8332 | Val Loss: 0.3735 | Val Acc: 0.8710\n",
      "Epoch 25/50 | Train Loss: 0.4342 | Train Acc: 0.8368 | Val Loss: 0.3970 | Val Acc: 0.8685\n",
      "Epoch 26/50 | Train Loss: 0.4330 | Train Acc: 0.8383 | Val Loss: 0.3909 | Val Acc: 0.8665\n",
      "Epoch 27/50 | Train Loss: 0.4331 | Train Acc: 0.8369 | Val Loss: 0.3748 | Val Acc: 0.8615\n",
      "Epoch 28/50 | Train Loss: 0.4272 | Train Acc: 0.8397 | Val Loss: 0.3706 | Val Acc: 0.8705\n",
      "Epoch 29/50 | Train Loss: 0.4256 | Train Acc: 0.8389 | Val Loss: 0.3712 | Val Acc: 0.8720\n",
      "Epoch 30/50 | Train Loss: 0.4287 | Train Acc: 0.8360 | Val Loss: 0.3704 | Val Acc: 0.8700\n",
      "Epoch 31/50 | Train Loss: 0.4288 | Train Acc: 0.8427 | Val Loss: 0.3804 | Val Acc: 0.8565\n",
      "Epoch 32/50 | Train Loss: 0.4105 | Train Acc: 0.8468 | Val Loss: 0.3640 | Val Acc: 0.8700\n",
      "Epoch 33/50 | Train Loss: 0.4162 | Train Acc: 0.8407 | Val Loss: 0.3820 | Val Acc: 0.8625\n",
      "Epoch 34/50 | Train Loss: 0.4190 | Train Acc: 0.8424 | Val Loss: 0.4193 | Val Acc: 0.8470\n",
      "Epoch 35/50 | Train Loss: 0.4128 | Train Acc: 0.8471 | Val Loss: 0.3747 | Val Acc: 0.8665\n",
      "Epoch 36/50 | Train Loss: 0.4025 | Train Acc: 0.8492 | Val Loss: 0.3762 | Val Acc: 0.8595\n",
      "Epoch 37/50 | Train Loss: 0.4059 | Train Acc: 0.8475 | Val Loss: 0.3546 | Val Acc: 0.8715\n",
      "Epoch 38/50 | Train Loss: 0.4026 | Train Acc: 0.8504 | Val Loss: 0.3736 | Val Acc: 0.8610\n",
      "Epoch 39/50 | Train Loss: 0.4028 | Train Acc: 0.8478 | Val Loss: 0.3939 | Val Acc: 0.8690\n",
      "Epoch 40/50 | Train Loss: 0.4011 | Train Acc: 0.8511 | Val Loss: 0.3941 | Val Acc: 0.8610\n",
      "Epoch 41/50 | Train Loss: 0.3875 | Train Acc: 0.8584 | Val Loss: 0.3650 | Val Acc: 0.8755\n",
      "Epoch 42/50 | Train Loss: 0.3979 | Train Acc: 0.8468 | Val Loss: 0.3793 | Val Acc: 0.8585\n",
      "Epoch 43/50 | Train Loss: 0.3848 | Train Acc: 0.8538 | Val Loss: 0.4012 | Val Acc: 0.8580\n",
      "Epoch 44/50 | Train Loss: 0.3979 | Train Acc: 0.8524 | Val Loss: 0.3639 | Val Acc: 0.8650\n",
      "Epoch 45/50 | Train Loss: 0.3939 | Train Acc: 0.8493 | Val Loss: 0.3807 | Val Acc: 0.8650\n",
      "Epoch 46/50 | Train Loss: 0.3966 | Train Acc: 0.8533 | Val Loss: 0.3616 | Val Acc: 0.8655\n",
      "Epoch 47/50 | Train Loss: 0.3885 | Train Acc: 0.8539 | Val Loss: 0.3805 | Val Acc: 0.8665\n",
      "Epoch 48/50 | Train Loss: 0.3912 | Train Acc: 0.8543 | Val Loss: 0.3570 | Val Acc: 0.8725\n",
      "Epoch 49/50 | Train Loss: 0.3910 | Train Acc: 0.8499 | Val Loss: 0.3558 | Val Acc: 0.8735\n",
      "Epoch 50/50 | Train Loss: 0.3923 | Train Acc: 0.8530 | Val Loss: 0.3516 | Val Acc: 0.8695\n",
      "MLP测试准确率: 0.8628\n",
      "训练耗时: 91.76 秒\n",
      "Total parameters: 235146\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T02:03:16.740875800Z",
     "start_time": "2026-04-08T02:03:12.073323400Z"
    }
   },
   "cell_type": "code",
   "source": "visualize_nearest_neighbors(mlp, train_loader, test_loader, device, model_type='mlp', k=5)",
   "id": "34996f47242d3977",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x3000 with 60 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:16:40.137690300Z",
     "start_time": "2026-04-08T01:14:56.421165400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "## 训练CNN\n",
    "cnn = CNN()\n",
    "start_time = time.time()\n",
    "cnn_train_losses, cnn_val_losses, cnn_train_accs, cnn_val_accs = train_model(cnn, train_loader, val_loader, epochs=epochs,lr=lr, device=device, model_type='cnn')\n",
    "elapsed = time.time() - start_time\n",
    "cnn.load_state_dict(torch.load(f'best_model_{get_model_class_name(cnn)}.pth'))\n",
    "cnn_test_acc = evaluate(cnn, test_loader, device, model_type='cnn')\n",
    "total_params = sum(p.numel() for p in cnn.parameters())\n",
    "print(f\"CNN测试准确率: {cnn_test_acc:.4f}\")\n",
    "print(f\"训练耗时: {elapsed:.2f} 秒\")\n",
    "print(f\"Total parameters: {total_params:}\")"
   ],
   "id": "ae661ae3ae4a32bd",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_19824\\2557972199.py:67: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  scheduler.step(val_loss)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1/50 | Train Loss: 0.7033 | Train Acc: 0.7406 | Val Loss: 0.4425 | Val Acc: 0.8390\n",
      "Epoch  2/50 | Train Loss: 0.4873 | Train Acc: 0.8190 | Val Loss: 0.3756 | Val Acc: 0.8545\n",
      "Epoch  3/50 | Train Loss: 0.4371 | Train Acc: 0.8397 | Val Loss: 0.3516 | Val Acc: 0.8680\n",
      "Epoch  4/50 | Train Loss: 0.4039 | Train Acc: 0.8513 | Val Loss: 0.3321 | Val Acc: 0.8770\n",
      "Epoch  5/50 | Train Loss: 0.3698 | Train Acc: 0.8669 | Val Loss: 0.3140 | Val Acc: 0.8845\n",
      "Epoch  6/50 | Train Loss: 0.3522 | Train Acc: 0.8691 | Val Loss: 0.3033 | Val Acc: 0.8930\n",
      "Epoch  7/50 | Train Loss: 0.3293 | Train Acc: 0.8747 | Val Loss: 0.2958 | Val Acc: 0.8915\n",
      "Epoch  8/50 | Train Loss: 0.3220 | Train Acc: 0.8799 | Val Loss: 0.3012 | Val Acc: 0.8855\n",
      "Epoch  9/50 | Train Loss: 0.3000 | Train Acc: 0.8858 | Val Loss: 0.2822 | Val Acc: 0.8995\n",
      "Epoch 10/50 | Train Loss: 0.2928 | Train Acc: 0.8901 | Val Loss: 0.2839 | Val Acc: 0.8960\n",
      "Epoch 11/50 | Train Loss: 0.2818 | Train Acc: 0.8929 | Val Loss: 0.2959 | Val Acc: 0.8915\n",
      "Epoch 12/50 | Train Loss: 0.2750 | Train Acc: 0.8944 | Val Loss: 0.2813 | Val Acc: 0.8970\n",
      "Epoch 13/50 | Train Loss: 0.2721 | Train Acc: 0.9000 | Val Loss: 0.2635 | Val Acc: 0.9025\n",
      "Epoch 14/50 | Train Loss: 0.2576 | Train Acc: 0.9031 | Val Loss: 0.2849 | Val Acc: 0.8980\n",
      "Epoch 15/50 | Train Loss: 0.2546 | Train Acc: 0.9059 | Val Loss: 0.2695 | Val Acc: 0.9005\n",
      "Epoch 16/50 | Train Loss: 0.2364 | Train Acc: 0.9111 | Val Loss: 0.2556 | Val Acc: 0.9080\n",
      "Epoch 17/50 | Train Loss: 0.2407 | Train Acc: 0.9061 | Val Loss: 0.2808 | Val Acc: 0.9020\n",
      "Epoch 18/50 | Train Loss: 0.2394 | Train Acc: 0.9078 | Val Loss: 0.2583 | Val Acc: 0.9075\n",
      "Epoch 19/50 | Train Loss: 0.2270 | Train Acc: 0.9138 | Val Loss: 0.2730 | Val Acc: 0.9025\n",
      "Epoch 20/50 | Train Loss: 0.2269 | Train Acc: 0.9157 | Val Loss: 0.2722 | Val Acc: 0.9045\n",
      "Epoch 21/50 | Train Loss: 0.2216 | Train Acc: 0.9153 | Val Loss: 0.2863 | Val Acc: 0.8950\n",
      "Epoch 22/50 | Train Loss: 0.2181 | Train Acc: 0.9164 | Val Loss: 0.2785 | Val Acc: 0.9040\n",
      "Epoch 23/50 | Train Loss: 0.2070 | Train Acc: 0.9194 | Val Loss: 0.2795 | Val Acc: 0.9085\n",
      "Epoch 24/50 | Train Loss: 0.2090 | Train Acc: 0.9193 | Val Loss: 0.2763 | Val Acc: 0.9060\n",
      "Epoch 25/50 | Train Loss: 0.1975 | Train Acc: 0.9225 | Val Loss: 0.2871 | Val Acc: 0.9045\n",
      "Epoch 26/50 | Train Loss: 0.2004 | Train Acc: 0.9223 | Val Loss: 0.2723 | Val Acc: 0.9070\n",
      "Epoch 27/50 | Train Loss: 0.1912 | Train Acc: 0.9250 | Val Loss: 0.3018 | Val Acc: 0.8970\n",
      "Epoch 28/50 | Train Loss: 0.1919 | Train Acc: 0.9259 | Val Loss: 0.2669 | Val Acc: 0.9085\n",
      "Epoch 29/50 | Train Loss: 0.1937 | Train Acc: 0.9268 | Val Loss: 0.2953 | Val Acc: 0.9080\n",
      "Epoch 30/50 | Train Loss: 0.1874 | Train Acc: 0.9276 | Val Loss: 0.2894 | Val Acc: 0.9080\n",
      "Epoch 31/50 | Train Loss: 0.1797 | Train Acc: 0.9302 | Val Loss: 0.2835 | Val Acc: 0.9090\n",
      "Epoch 32/50 | Train Loss: 0.1824 | Train Acc: 0.9322 | Val Loss: 0.2610 | Val Acc: 0.9125\n",
      "Epoch 33/50 | Train Loss: 0.1763 | Train Acc: 0.9320 | Val Loss: 0.2730 | Val Acc: 0.9130\n",
      "Epoch 34/50 | Train Loss: 0.1665 | Train Acc: 0.9363 | Val Loss: 0.3033 | Val Acc: 0.9060\n",
      "Epoch 35/50 | Train Loss: 0.1705 | Train Acc: 0.9374 | Val Loss: 0.2832 | Val Acc: 0.9130\n",
      "Epoch 36/50 | Train Loss: 0.1675 | Train Acc: 0.9358 | Val Loss: 0.2781 | Val Acc: 0.9110\n",
      "Epoch 37/50 | Train Loss: 0.1646 | Train Acc: 0.9377 | Val Loss: 0.2742 | Val Acc: 0.9105\n",
      "Epoch 38/50 | Train Loss: 0.1629 | Train Acc: 0.9370 | Val Loss: 0.2697 | Val Acc: 0.9190\n",
      "Epoch 39/50 | Train Loss: 0.1568 | Train Acc: 0.9384 | Val Loss: 0.2987 | Val Acc: 0.9075\n",
      "Epoch 40/50 | Train Loss: 0.1596 | Train Acc: 0.9401 | Val Loss: 0.2880 | Val Acc: 0.9055\n",
      "Epoch 41/50 | Train Loss: 0.1551 | Train Acc: 0.9401 | Val Loss: 0.2838 | Val Acc: 0.9130\n",
      "Epoch 42/50 | Train Loss: 0.1525 | Train Acc: 0.9431 | Val Loss: 0.2990 | Val Acc: 0.9130\n",
      "Epoch 43/50 | Train Loss: 0.1482 | Train Acc: 0.9419 | Val Loss: 0.3098 | Val Acc: 0.9140\n",
      "Epoch 44/50 | Train Loss: 0.1463 | Train Acc: 0.9434 | Val Loss: 0.3133 | Val Acc: 0.9000\n",
      "Epoch 45/50 | Train Loss: 0.1451 | Train Acc: 0.9483 | Val Loss: 0.3228 | Val Acc: 0.9075\n",
      "Epoch 46/50 | Train Loss: 0.1439 | Train Acc: 0.9449 | Val Loss: 0.3161 | Val Acc: 0.9120\n",
      "Epoch 47/50 | Train Loss: 0.1439 | Train Acc: 0.9455 | Val Loss: 0.3073 | Val Acc: 0.9130\n",
      "Epoch 48/50 | Train Loss: 0.1402 | Train Acc: 0.9479 | Val Loss: 0.3340 | Val Acc: 0.8990\n",
      "Epoch 49/50 | Train Loss: 0.1423 | Train Acc: 0.9446 | Val Loss: 0.3046 | Val Acc: 0.9125\n",
      "Epoch 50/50 | Train Loss: 0.1413 | Train Acc: 0.9469 | Val Loss: 0.2813 | Val Acc: 0.9110\n",
      "CNN测试准确率: 0.9093\n",
      "训练耗时: 103.18 秒\n",
      "Total parameters: 824458\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T02:03:11.883125400Z",
     "start_time": "2026-04-08T02:03:07.377071700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sample_image, _ = test_dataset[0]\n",
    "visualize_nearest_neighbors(cnn, train_loader, test_loader, device, model_type='cnn', k=5)\n",
    "visualize_first_conv_filters(cnn,model_type='cnn')\n",
    "visualize_first_conv_feature_maps(cnn, sample_image, device, model_type='cnn')"
   ],
   "id": "c5e7966cbaddef9e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x3000 with 60 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 32 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T01:19:34.919174700Z",
     "start_time": "2026-04-08T01:16:44.669800800Z"
    }
   },
   "cell_type": "code",
   "source": [
    "hybrid = HybridCNN(\n",
    "    img_size=28,\n",
    "    in_channels=1,\n",
    "    patch_size=4,          # 更小的 patch\n",
    "    embed_dim=64,          # 更大的特征维度\n",
    "    num_heads=8,\n",
    "    num_layers=3,          # 减少层数防止过拟合\n",
    "    num_classes=10,\n",
    "    cnn_out_channels=64\n",
    ")\n",
    "\n",
    "# 训练\n",
    "start_time = time.time()\n",
    "hybrid_train_losses, hybrid_val_losses, hybrid_train_accs, hybrid_val_accs = train_model(\n",
    "    hybrid, train_loader, val_loader, epochs=epochs, lr=lr, device=device,  model_type='hybrid'\n",
    ")\n",
    "elapsed = time.time() - start_time\n",
    "\n",
    "# 测试最佳模型\n",
    "hybrid.load_state_dict(torch.load(f'best_model_{get_model_class_name(hybrid)}.pth'))\n",
    "hybrid_test_acc = evaluate(hybrid, test_loader, device, model_type='hybrid')\n",
    "total_params = sum(p.numel() for p in hybrid.parameters())\n",
    "print(f\"HybridCNN测试准确率: {hybrid_test_acc:.4f}\")\n",
    "print(f\"训练耗时: {elapsed:.2f} 秒\")\n",
    "print(f\"Total parameters: {total_params:}\")"
   ],
   "id": "f269a07f4d24bbd1",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_19824\\2557972199.py:67: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.\n",
      "  scheduler.step(val_loss)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1/50 | Train Loss: 1.0699 | Train Acc: 0.5958 | Val Loss: 0.6888 | Val Acc: 0.7480\n",
      "Epoch  2/50 | Train Loss: 0.6966 | Train Acc: 0.7323 | Val Loss: 0.6399 | Val Acc: 0.7660\n",
      "Epoch  3/50 | Train Loss: 0.6066 | Train Acc: 0.7725 | Val Loss: 0.5415 | Val Acc: 0.8185\n",
      "Epoch  4/50 | Train Loss: 0.5620 | Train Acc: 0.7906 | Val Loss: 0.5710 | Val Acc: 0.7895\n",
      "Epoch  5/50 | Train Loss: 0.5172 | Train Acc: 0.8084 | Val Loss: 0.4506 | Val Acc: 0.8440\n",
      "Epoch  6/50 | Train Loss: 0.4821 | Train Acc: 0.8231 | Val Loss: 0.5019 | Val Acc: 0.8185\n",
      "Epoch  7/50 | Train Loss: 0.4517 | Train Acc: 0.8353 | Val Loss: 0.4670 | Val Acc: 0.8400\n",
      "Epoch  8/50 | Train Loss: 0.4354 | Train Acc: 0.8398 | Val Loss: 0.3990 | Val Acc: 0.8625\n",
      "Epoch  9/50 | Train Loss: 0.4274 | Train Acc: 0.8432 | Val Loss: 0.4404 | Val Acc: 0.8460\n",
      "Epoch 10/50 | Train Loss: 0.4117 | Train Acc: 0.8482 | Val Loss: 0.3867 | Val Acc: 0.8615\n",
      "Epoch 11/50 | Train Loss: 0.3909 | Train Acc: 0.8577 | Val Loss: 0.3925 | Val Acc: 0.8595\n",
      "Epoch 12/50 | Train Loss: 0.3772 | Train Acc: 0.8615 | Val Loss: 0.3866 | Val Acc: 0.8625\n",
      "Epoch 13/50 | Train Loss: 0.3755 | Train Acc: 0.8630 | Val Loss: 0.3923 | Val Acc: 0.8660\n",
      "Epoch 14/50 | Train Loss: 0.3718 | Train Acc: 0.8638 | Val Loss: 0.3781 | Val Acc: 0.8600\n",
      "Epoch 15/50 | Train Loss: 0.3677 | Train Acc: 0.8658 | Val Loss: 0.3604 | Val Acc: 0.8680\n",
      "Epoch 16/50 | Train Loss: 0.3485 | Train Acc: 0.8698 | Val Loss: 0.4171 | Val Acc: 0.8650\n",
      "Epoch 17/50 | Train Loss: 0.3490 | Train Acc: 0.8724 | Val Loss: 0.3546 | Val Acc: 0.8775\n",
      "Epoch 18/50 | Train Loss: 0.3469 | Train Acc: 0.8712 | Val Loss: 0.3602 | Val Acc: 0.8775\n",
      "Epoch 19/50 | Train Loss: 0.3311 | Train Acc: 0.8798 | Val Loss: 0.3723 | Val Acc: 0.8725\n",
      "Epoch 20/50 | Train Loss: 0.3323 | Train Acc: 0.8781 | Val Loss: 0.3537 | Val Acc: 0.8795\n",
      "Epoch 21/50 | Train Loss: 0.3200 | Train Acc: 0.8844 | Val Loss: 0.3499 | Val Acc: 0.8805\n",
      "Epoch 22/50 | Train Loss: 0.3253 | Train Acc: 0.8805 | Val Loss: 0.3676 | Val Acc: 0.8685\n",
      "Epoch 23/50 | Train Loss: 0.3140 | Train Acc: 0.8813 | Val Loss: 0.3716 | Val Acc: 0.8725\n",
      "Epoch 24/50 | Train Loss: 0.3113 | Train Acc: 0.8841 | Val Loss: 0.3838 | Val Acc: 0.8690\n",
      "Epoch 25/50 | Train Loss: 0.3050 | Train Acc: 0.8897 | Val Loss: 0.3536 | Val Acc: 0.8685\n",
      "Epoch 26/50 | Train Loss: 0.3007 | Train Acc: 0.8858 | Val Loss: 0.3445 | Val Acc: 0.8815\n",
      "Epoch 27/50 | Train Loss: 0.2946 | Train Acc: 0.8908 | Val Loss: 0.3549 | Val Acc: 0.8785\n",
      "Epoch 28/50 | Train Loss: 0.2910 | Train Acc: 0.8914 | Val Loss: 0.4360 | Val Acc: 0.8550\n",
      "Epoch 29/50 | Train Loss: 0.2857 | Train Acc: 0.8926 | Val Loss: 0.3352 | Val Acc: 0.8810\n",
      "Epoch 30/50 | Train Loss: 0.2859 | Train Acc: 0.8955 | Val Loss: 0.3302 | Val Acc: 0.8805\n",
      "Epoch 31/50 | Train Loss: 0.2823 | Train Acc: 0.8946 | Val Loss: 0.3381 | Val Acc: 0.8800\n",
      "Epoch 32/50 | Train Loss: 0.2770 | Train Acc: 0.8984 | Val Loss: 0.3154 | Val Acc: 0.8850\n",
      "Epoch 33/50 | Train Loss: 0.2819 | Train Acc: 0.8949 | Val Loss: 0.3430 | Val Acc: 0.8745\n",
      "Epoch 34/50 | Train Loss: 0.2760 | Train Acc: 0.8983 | Val Loss: 0.3351 | Val Acc: 0.8835\n",
      "Epoch 35/50 | Train Loss: 0.2723 | Train Acc: 0.8987 | Val Loss: 0.3436 | Val Acc: 0.8790\n",
      "Epoch 36/50 | Train Loss: 0.2739 | Train Acc: 0.8974 | Val Loss: 0.3275 | Val Acc: 0.8780\n",
      "Epoch 37/50 | Train Loss: 0.2686 | Train Acc: 0.8994 | Val Loss: 0.3030 | Val Acc: 0.8920\n",
      "Epoch 38/50 | Train Loss: 0.2598 | Train Acc: 0.9000 | Val Loss: 0.3275 | Val Acc: 0.8780\n",
      "Epoch 39/50 | Train Loss: 0.2616 | Train Acc: 0.9022 | Val Loss: 0.3433 | Val Acc: 0.8790\n",
      "Epoch 40/50 | Train Loss: 0.2567 | Train Acc: 0.9035 | Val Loss: 0.3278 | Val Acc: 0.8855\n",
      "Epoch 41/50 | Train Loss: 0.2624 | Train Acc: 0.9005 | Val Loss: 0.3358 | Val Acc: 0.8795\n",
      "Epoch 42/50 | Train Loss: 0.2522 | Train Acc: 0.9066 | Val Loss: 0.3155 | Val Acc: 0.8920\n",
      "Epoch 43/50 | Train Loss: 0.2512 | Train Acc: 0.9066 | Val Loss: 0.3066 | Val Acc: 0.8900\n",
      "Epoch 44/50 | Train Loss: 0.2503 | Train Acc: 0.9075 | Val Loss: 0.3394 | Val Acc: 0.8880\n",
      "Epoch 45/50 | Train Loss: 0.2522 | Train Acc: 0.9048 | Val Loss: 0.3258 | Val Acc: 0.8860\n",
      "Epoch 46/50 | Train Loss: 0.2370 | Train Acc: 0.9111 | Val Loss: 0.3531 | Val Acc: 0.8800\n",
      "Epoch 47/50 | Train Loss: 0.2470 | Train Acc: 0.9064 | Val Loss: 0.3389 | Val Acc: 0.8835\n",
      "Epoch 48/50 | Train Loss: 0.2324 | Train Acc: 0.9133 | Val Loss: 0.3050 | Val Acc: 0.8940\n",
      "Epoch 49/50 | Train Loss: 0.2407 | Train Acc: 0.9092 | Val Loss: 0.3104 | Val Acc: 0.8895\n",
      "Epoch 50/50 | Train Loss: 0.2355 | Train Acc: 0.9102 | Val Loss: 0.3153 | Val Acc: 0.8895\n",
      "HybridCNN测试准确率: 0.8962\n",
      "训练耗时: 169.41 秒\n",
      "Total parameters: 235338\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T02:19:08.999481400Z",
     "start_time": "2026-04-08T02:19:03.444275600Z"
    }
   },
   "cell_type": "code",
   "source": [
    "visualize_nearest_neighbors(hybrid, train_loader, test_loader, device, model_type='hybrid', k=5)\n",
    "visualize_first_conv_feature_maps(hybrid, sample_image, device, model_type='hybrid')\n",
    "visualize_transformer_attention_heads(hybrid, sample_image, device, layer_idx=0, model_type='hybrid')"
   ],
   "id": "6eafd50fb364ff1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x3000 with 60 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 10 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-04-08T02:02:45.546242300Z",
     "start_time": "2026-04-08T02:02:45.235580300Z"
    }
   },
   "cell_type": "code",
   "source": [
    "epochs = range(1, 50 + 1)\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs, mlp_train_accs, label='MLP Train')\n",
    "plt.plot(epochs, mlp_val_accs, label='MLP Val')\n",
    "plt.plot(epochs, cnn_train_accs, label='CNN Train')\n",
    "plt.plot(epochs, cnn_val_accs, label='CNN Val')\n",
    "plt.plot(epochs, hybrid_train_accs, label='HybridCNN Train')\n",
    "plt.plot(epochs, hybrid_val_accs, label='HybridCNN Val')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs, mlp_train_losses, label='MLP Train Loss')\n",
    "plt.plot(epochs, mlp_val_losses, label='MLP Val Loss')\n",
    "plt.plot(epochs, cnn_train_losses, label='CNN Train Loss')\n",
    "plt.plot(epochs, cnn_val_losses, label='CNN Val Loss')\n",
    "plt.plot(epochs, hybrid_train_losses, label='HybridCNN Train Loss')\n",
    "plt.plot(epochs, hybrid_val_losses, label='HybridCNN Val Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Loss')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('learning_curve.png')\n",
    "plt.show()\n",
    "\n",
    "# 打印最终结果\n",
    "print(\"\\n===== 最终测试准确率 =====\")\n",
    "print(f\"MLP: {mlp_test_acc:.4f}\")\n",
    "print(f\"CNN: {cnn_test_acc:.4f}\")\n",
    "print(f\"HybridCNN: {hybrid_test_acc:.4f}\")"
   ],
   "id": "adc724a2af467f13",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===== 最终测试准确率 =====\n",
      "MLP: 0.8628\n",
      "CNN: 0.9093\n",
      "HybridCNN: 0.8962\n"
     ]
    }
   ],
   "execution_count": 26
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}